SoroushVahidi/scidocs huggingface dataset:
A Three-Winding Coupled-Inductor DC–DC Converter Topology With High Voltage Gain and Reduced Switch Stress
(2018)

This paper presents a dc–dc boost converter topology for low input and high output voltage applications, such as photovoltaic systems, fuel cell systems, high-intensity discharge lamp, and electric vehicles. The suggested configuration consists of a three-winding coupled-inductor, a single switch and two hybrid voltage multiplier cells. Furthermore, two independent hybrid voltage multiplier cells are in parallel when the single switch S is turned on, and they are in series when the switch S is turned off. So, the advantages of the proposed converter structure are summarized as follows: 1) A coupled inductor with three windings is introduced in the presented converter structure. The two secondary windings of the coupled inductor are, respectively, used to form a hybrid multiplier cell on the one hand, on the other hand, it increases the control freedom of the voltage gain, enhances the utility rate of magnetic core and power density, and reduces the stress of power components to provide a stable constant dc output voltage. 2) The two hybrid multiplier cells can absorb synchronously the energy of stray inductance, which not only reduces the current stress of corresponding diodes, but also greatly alleviates the spike voltage of the main switch, which improves the efficiency. 3) The two hybrid multiplier cells are connected in series to supply power energy for the load, so the voltage gain is extended greatly due to this particular structure. Thus, low-voltage low-conduction-loss devices can be selected and the reverse-recovery currents within the diodes are inhibited. The operating principles and the steady state analyses of the proposed converter are discussed in detail. Finally, a test prototype has been implemented in the laboratory, and the simulated and experimental results show satisfactory agreement with the theoretical analysis.
Twitter Sentiment Analysis: A Bootstrap Ensemble Framework
(2013)

Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.
DeFlaker: Automatically Detecting Flaky Tests
(2014)

Developers often run tests to check that their latest changes to a code repository did not break any previously working functionality. Ideally, any new test failures would indicate regressions caused by the latest changes. However, some test failures may not be due to the latest changes but due to non-determinism in the tests, popularly called flaky tests. The typical way to detect flaky tests is to rerun failing tests repeatedly. Unfortunately, rerunning failing tests can be costly and can slow down the development cycle.
 We present the first extensive evaluation of rerunning failing tests and propose a new technique, called DeFlaker, that detects if a test failure is due to a flaky test without rerunning and with very low runtime overhead. DeFlaker monitors the coverage of latest code changes and marks as flaky any newly failing test that did not execute any of the changes. We deployed DeFlaker live, in the build process of 96 Java projects on TravisCI, and found 87 previously unknown flaky tests in 10 of these projects. We also ran experiments on project histories, where DeFlaker detected 1, 874 flaky tests from 4, 846 failures, with a low false alarm rate (1.5%). DeFlaker had a higher recall (95.5% vs. 23%) of confirmed flaky tests than Maven's default flaky test detector.
Understanding the influence and service type of trusted third party on consumers' online trust: evidence from Australian B2C marketplace
(2016)

In this study, the trusted third party (TTP) in Australia's B2C marketplace is studied and the factors influencing consumers' trust behaviour are examined from the perspective of consumers' online trust. Based on the literature review and combined with the development status and background of Australia's e-commerce, underpinned by the Theory of Planned Behaviour (TPB) and a conceptual trust model, this paper expatiates the specific factors and influence mechanism of TTP on consumers' trust behaviour. Also this paper explains two different functions of TTP to solve the online trust problem faced by consumers. Meanwhile, this paper summarizes five different types of services provided by TTPs during the establishment of the trust relationship. Finally, the present study selects 100 B2C enterprises by the simple random sampling method and makes a detailed analysis of their TTPs, to verify the services and functions of the proposed TTP in the trust model. This study is of some significance for comprehending the influence mechanism, functions and services of TTPs on consumers' trust behaviour in the realistic Australian B2C environment.
Exploration and Mapping with Autonomous Robot Teams Results from the MAGIC 2010 Competition
(2012)

The potential impact of autonomous robotics is magnified when those robots are deployed in teams: a team of cooperating robots can greatly increase the effectiveness of a human working alone, making short work of search-and-rescue and reconnaissance tasks. To achieve this potential, however, a number of challenging problems ranging from multi-robot planning, state estimation, object detection, and human-robot interfaces must first be solved. The MAGIC 2010 competition, like the DARPA grand challenges that preceded it, presented a formidable robotics problem designed to foster fundamental advances in these difficult areas. MAGIC asked teams of robots to collaboratively explore and map a 500× 500m area, detect and track benign and dangerous objects, and collaborate with human commanders while respecting their cognitive limits. This paper describes our winning entry in the MAGIC contest, where we fielded a team of 14 autonomous robots supervised by two human operators. While the challenges in MAGIC were diverse, we believe that cooperative multi-robot state estimation is ultimately the critical factor in building a successful system. In this paper, we describe our system and some of the technological advances that we believe were responsible for our success. We also contrast our approach to those of other teams. a)
A segmented claw-pole motor for traction applications considering recycling aspects
(2013)

With the expansion of the fleet of electric and hybrid electric vehicles worldwide, it is of interest to consider recycling aspects of the parts that are introduced in these new vehicles. This paper focuses on the design of an electrical machine, a claw-pole motor, considering recycling of its components. The suggested design has segmented core parts of soft magnetic composites which yield a design very suitable for recycling as the core material is brittle and it thus simplifies the access of the copper winding. The windings are pre-formed ring-coils with a high fill-factor and with no end-windings. Through the use of direct water cooling, employing space between the claws, the current density can be high, and the machine can be compact. The results of finite element simulations of the electromagnetic properties (previously validated against measurements) show that a high performance recyclable motor with ratings above 10 kW can be made. In comparison with a commercial motor, the claw-pole motor has similar performance, a higher core and magnet weight and a lower copper weight. It also has an expected advantage regarding manufacturing cost and cost of recycling and it has lower copper loss.
Force Jacket: Pneumatically-Actuated Jacket for Embodied Haptic Experiences
(2018)

Immersive experiences seek to engage the full sensory system in ways that words, pictures, or touch alone cannot. With respect to the haptic system, however, physical feedback has been provided primarily with handheld tactile experiences or vibration-based designs, largely ignoring both pressure receptors and the full upper-body area as conduits for expressing meaning that is consistent with sight and sound. We extend the potential for immersion along these dimensions with the Force Jacket, a novel array of pneumatically-actuated airbags and force sensors that provide precisely directed force and high frequency vibrations to the upper body. We describe the pneumatic hardware and force control algorithms, user studies to verify perception of airbag location and pressure magnitude, and subsequent studies to define full-torso, pressure and vibration-based feel effects such as punch, hug, and snake moving across the body. We also discuss the use of those effects in prototype virtual reality applications.
Adoption of business continuity planning processes in IT service management
(2008)

For any fault of the same severity level, traditional fault discovery and notification tools provide equal weighting from business points of view. To improve the fault correlation from business perspectives, we proposed a framework to automate network and system alerts with respect to its business service impact for proactive notification to IT operations management. This paper outlines the value of business continuity planning (BCP) during the course of service impact analysis, placing particular emphasis on the business perspective in the processes of IT service management. The framework explicitly employs BCP relevant processes in order to identify the relationships between business services and IT resources A practical case in IT operations to illustrate the concept was then conducted.
ADMIRAL: A Data Mining Based Financial Trading System
(2007)

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions
Linear cross-modal hashing for efficient multimedia search
(2013)

Most existing cross-modal hashing methods suffer from the scalability issue in the training phase. In this paper, we propose a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals. Taking both the intra-similarity in each modal and the inter-similarity across different modals into consideration, the proposed approach aims at effectively learning hash functions from large-scale training datasets. More specifically, for each modal, we first partition the training data into $k$ clusters and then represent each training data point with its distances to $k$ centroids of the clusters. Interestingly, such a k-dimensional data representation can reduce the time complexity of the training phase from traditional O(n2) or higher to O(n), where $n$ is the training data size, leading to practical learning on large-scale datasets. We further prove that this new representation preserves the intra-similarity in each modal. To preserve the inter-similarity among data points across different modals, we transform the derived data representations into a common binary subspace in which binary codes from all the modals are "consistent" and comparable. nThe transformation simultaneously outputs the hash functions for all modals, which are used to convert unseen data into binary codes. Given a query of one modal, it is first mapped into the binary codes using the modal's hash functions, followed by matching the database binary codes of any other modals. Experimental results on two benchmark datasets confirm the scalability and the effectiveness of the proposed approach in comparison with the state of the art.
Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments
(2010)

In this report we provide an overview of the open source Armadillo C++ linear algebra library (matrix maths). The library aims to have a good balance between speed and ease of use, and is useful if C++ is the language of choice (due to speed and/or integration capabilities), rather than another language like Matlab or Octave. In particular, Armadillo can be used for fast prototyping and computationally intensive experiments, while at the same time allowing for relatively painless transition of research code into production environments. It is distributed under a license that is applicable in both open source and proprietary software development contexts. The library supports integer, floating point and complex numbers, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK, or one its high-performance drop-in replacements, such as MKL from Intel or ACML from AMD. A delayed evaluation approach is employed (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is accomplished through C++ template meta-programming. Performance comparisons suggest that the library is considerably faster than Matlab and Octave, as well as previous C++ libraries such as IT++ and Newmat. This report reflects a subset of the functionality present in Armadillo v2.4. Armadillo can be downloaded from: • http://arma.sourceforge.net If you use Armadillo in your research and/or software, we would appreciate a citation to this document. Please cite as: • Conrad Sanderson. Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report, NICTA, 2010. Armadillo: An Open Source C++ Linear Algebra Library NICTA Technical Report
An optimal method for stochastic composite optimization
(2012)

This paper considers an important class of convex programming (CP) problems, namely, the stochastic composite optimization (SCO), whose objective function is given by the summation of general nonsmooth and smooth stochastic components. Since SCO covers non-smooth, smooth and stochastic CP as certain special cases, a valid lower bound on the rate of convergence for solving these problems is known from the classic complexity theory of convex programming. Note however that the optimization algorithms that can achieve this lower bound had never been developed. In this paper, we show that the simple mirror-descent stochastic approximation method exhibits the best-known rate of convergence for solving these problems. Our major contribution is to introduce the accelerated stochastic approximation (AC-SA) algorithm based on Nesterov’s optimal method for smooth CP [32,34], and show that the AC-SA algorithm can achieve the aforementioned lower bound on the rate of convergence for SCO. To the best of our knowledge, it is also the first universally optimal algorithm in the literature for solving non-smooth, smooth and stochastic CP problems. We illustrate the significant advantages of the AC-SA algorithm over existing methods in the context of solving a special but broad class of stochastic programming problems.
Universal approximation bounds for superpositions of a sigmoidal function
(1993)

Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one layer of sigmoidal nonlinearities achieve inte­ grated squared error of order O(l/n), where n is the number of nodes. The function appruximated is assumed to have a bound on the first moment of the magnitude distribution of the Fourier transform. The nonlinear parameters associated with the sigmoidal nodes, as well as the parameters of linear combination, are adjusted in the approximation. In contrast, it is shown that for series expansions with n terms, in which only the parameters of linear combination are adjusted, the integrated squared approximation error cannot be made smaller than order 1/n2/d uniformly for functions satisfying the same smoothness assumption, where d is the dimension of the input to the function. For the class of functions examined here, the approximation rate and the parsimony of the parameterization of the networks are surprisingly advantageous in high-dimensional settings.
Greed is good: algorithmic results for sparse approximation
(2004)

This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho's basis pursuit (BP) paradigm can recover the optimal representation of an exactly sparse signal. It leverages this theory to show that both OMP and BP succeed for every sparse input signal from a wide class of dictionaries. These quasi-incoherent dictionaries offer a natural generalization of incoherent dictionaries, and the cumulative coherence function is introduced to quantify the level of incoherence. This analysis unifies all the recent results on BP and extends them to OMP. Furthermore, the paper develops a sufficient condition under which OMP can identify atoms from an optimal approximation of a nonsparse signal. From there, it argues that OMP is an approximation algorithm for the sparse problem over a quasi-incoherent dictionary. That is, for every input signal, OMP calculates a sparse approximant whose error is only a small factor worse than the minimal error that can be attained with the same number of terms.
Primal-dual subgradient methods for convex problems
(2009)

In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem. Besides other advantages, this useful feature provides the methods with a reliable stopping criterion. The proposed schemes differ from the classical approaches (divergent series methods, mirror descent methods) by presence of two control sequences. The first sequence is responsible for aggregating the support functions in the dual space, and the second one establishes a dynamically updated scale between the primal and dual spaces. This additional flexibility allows to guarantee a boundedness of the sequence of primal test points even in the case of unbounded feasible set. We present the variants of subgradient schemes for nonsmooth convex minimization, minimax problems, saddle point problems, variational inequalities, and stochastic optimization. In all situations our methods are proved to be optimal from the view point of worst-case black-box lower complexity bounds.
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
(2016)

Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM’s performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.
Marine debris in central California: quantifying type and abundance of beach litter in Monterey Bay, CA.
(2013)

Monitoring beach litter is essential for reducing ecological threats towards humans and wildlife. In Monterey Bay, CA information on seasonal and spatial patterns is understudied. Central California's coastal managers require reliable information on debris abundance, distribution, and type, to support policy aimed at reducing litter. We developed a survey method that allowed for trained citizen scientists to quantify the types and abundance of beach litter. Sampling occurred from July 2009-June 2010. Litter abundance ranged from 0.03 to 17.1 items m(-2). Using a mixed model approach, we found season and location have the greatest effect on litter abundance. Styrofoam, the most numerically abundant item, made up 41% of the total amount of litter. Unexpected items included fertilizer pellets. The results of this study provide a baseline on the types and abundance of litter on the central coast and have directly supported policy banning Styrofoam take out containers from local municipalities.
Issues and Perspectives in Meditation Research: In Search for a Definition
(2012)

Despite the growing interest in the neurobiological correlates of meditation, most research has omitted to take into account the underlying philosophical aspects of meditation and its wider implications. This, in turn, is reflected in issues surrounding definition, study design, and outcomes. Here, I highlight the often ignored but important aspect of definition in the existing scholarship on neuroscience and meditation practice. For a satisfactory account of a neuroscience of meditation, we must aim to retrieve an operational definition that is inclusive of a traditional ontological description as well as the modern neurocognitive account of the phenomena. Moving beyond examining the effects of meditation practice, to take a potential step forward in the direction to establish how meditation works, it becomes crucial to appraise the philosophical positions that underlie the phenomenology of meditation in the originating traditions. This endeavor may challenge our intuitions and concepts in either directions, but issues pertaining to definition, design, and validity of response measures are extremely important for the evolution of the field and will provide a much-needed context and framework for meditation based interventions.